/**
 * Copyright (c) 2011, The University of Southampton and the individual contributors.
 * All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without modification,
 * are permitted provided that the following conditions are met:
 *
 *   *  Redistributions of source code must retain the above copyright notice,
 *      this list of conditions and the following disclaimer.
 *
 *   *  Redistributions in binary form must reproduce the above copyright notice,
 *      this list of conditions and the following disclaimer in the documentation
 *      and/or other materials provided with the distribution.
 *
 *   *  Neither the name of the University of Southampton nor the names of its
 *      contributors may be used to endorse or promote products derived from this
 *      software without specific prior written permission.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
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 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */
package org.openimaj.ml.clustering.rac;

import java.util.SortedMap;
import java.util.TreeMap;

import org.apache.commons.math.FunctionEvaluationException;
import org.apache.commons.math.analysis.UnivariateRealFunction;

/**
 * Similar to {@link IntRAC} but explicitly specify the limit the number of
 * clusters. Attempts to replace clusters which are "closer" with those that are
 * "further".
 * <p>
 * As with the {@link IntRAC} this is a combined clusterer, clusters and
 * assigner. See the {@link IntRAC} documentation for more details.
 * 
 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
 */
public class ClusterLimitedIntRAC extends IntRAC {
        static class ClusterMinimisationFunction implements UnivariateRealFunction {
                private int[][] distances;
                private int[][] samples;
                private int nClusters;

                public ClusterMinimisationFunction(int[][] samples, int[][] distances, int nClusters) {
                        this.distances = distances;
                        this.samples = samples;
                        this.nClusters = nClusters;
                }

                @Override
                public double value(double radius) throws FunctionEvaluationException {
                        final ClusterLimitedIntRAC r = new ClusterLimitedIntRAC(radius);
                        r.train(samples, distances);
                        final int diff = this.nClusters - r.numClusters();
                        return diff;
                }

        }

        private int expectedClusters;
        private SortedMap<Float, Integer> thresholdOvershots;

        /**
         * Sets the expected number of clusters to 100 and radius to 128.
         */
        public ClusterLimitedIntRAC() {
                super();
                this.expectedClusters = 100;
                thresholdOvershots = new TreeMap<Float, Integer>();
        }

        /**
         * Set the number of clusters to 100.
         * 
         * @param radiusSquared
         */
        public ClusterLimitedIntRAC(double radiusSquared) {
                super(radiusSquared);
                this.expectedClusters = 100;
                thresholdOvershots = new TreeMap<Float, Integer>();
        }

        /**
         * Attempt to learn the threshold and uses this as an expected number of
         * clusters.
         * 
         * @param bKeys
         * @param subSamples
         * @param nClusters
         */
        public ClusterLimitedIntRAC(int[][] bKeys, int subSamples, int nClusters) {
                super(bKeys, subSamples, nClusters);
                this.expectedClusters = (int) ((((float) nClusters) / subSamples) * bKeys.length);
        }

        @Override
        public ClusterLimitedIntRAC cluster(int[][] data) {
                int foundLength = this.nDims;

                for (final int[] entry : data) {
                        if (foundLength == -1)
                                foundLength = entry.length;

                        // all the data entries must be the same length otherwise this
                        // doesn't make sense
                        if (foundLength != entry.length) {
                                throw new RuntimeException();
                        }

                        boolean found = false;
                        float minDiff = 0;

                        for (final int[] existing : this.codebook) {
                                float distance = distanceEuclidianSquared(entry, existing);
                                if (distance < threshold) {
                                        found = true;
                                        break;
                                }
                                distance = (float) (distance - threshold);
                                if (minDiff == 0 || distance < minDiff)
                                        minDiff = distance;
                        }

                        if (!found) {
                                if (this.numClusters() >= this.expectedClusters) {
                                        // Remove the current smallest distance with this centroid
                                        final Float smallestDistance = this.thresholdOvershots.firstKey();
                                        if (smallestDistance < minDiff) {

                                                final Integer index = this.thresholdOvershots.get(smallestDistance);
                                                this.codebook.remove((int) index);
                                                this.codebook.add(index, entry);
                                                this.thresholdOvershots.remove(smallestDistance);
                                                this.thresholdOvershots.put(minDiff, this.numClusters() - 1);
                                                // System.out.println("I have replaced a less significant distance, new least significant distance is "
                                                // + this.thresholdOvershots.firstKey());
                                        }
                                } else {
                                        this.codebook.add(entry);
                                        if (this.numClusters() % 1000 == 0) {
                                                System.out.println("Codebook increased to size " + this.numClusters());
                                                System.out.println("with nSamples = " + this.totalSamples);
                                        }
                                        this.thresholdOvershots.put(minDiff, this.numClusters() - 1);
                                }

                        }
                        this.totalSamples += 1;
                }
                this.nDims = foundLength;

                return this;
        }
}